Meta Data Scientist Interview Preparation Guide - Senior Level (2026)

Data Scientist

Interview Rounds

Recruiter Screening

30 min3 focus topicsculture fit

What to Expect

This is your initial conversation with a Meta recruiter. The focus is on verifying your background, understanding your career motivation, assessing cultural alignment with Meta, and logistical details. The recruiter will walk you through the interview process, discuss your visa or citizenship status, and gauge your interest level. This round is conversational rather than technical and serves as a qualification gate before investing in the phone screen. The recruiter may ask light technical questions to validate baseline competency but will not conduct deep technical assessment.

Tips & Advice

Be clear and concise about your background and why Meta specifically interests you. Research Meta's mission, products, and culture beforehand. Have specific examples ready of why you're excited about data science at Meta. Be honest about logistics (location, visa, relocation willingness) to avoid surprises later. Show genuine interest in the role and company. This is also your chance to ask clarifying questions about the team, the problems you'd work on, and the interview process timeline.

Focus Topics

Logistics and Availability

Be prepared to discuss visa/citizenship status, willingness to relocate, remote vs. office preferences, and availability for interviews. Provide clear, direct answers. For senior roles, discuss your scheduling flexibility for the interview process and availability to start.

Practice Interview

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Resume and Career Journey

Be prepared to walk through your resume concisely, highlighting key projects, technical skills, and progression. Focus on the impact you've had—e.g., 'improved model accuracy by 15%, leading to $2M revenue increase' rather than just listing tasks. For senior level, emphasize projects where you owned the end-to-end analysis, mentored junior team members, or influenced strategic decisions.

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Why Meta?

Articulate a genuine reason for wanting to work at Meta as a Data Scientist. Reference Meta's products, scale, technical challenges, or mission. Avoid generic answers. Show that you understand what Meta does and why working there appeals to you specifically. For senior level, discuss how your experience aligns with scaling data science at Meta's scale.

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Technical Phone Screen (Initial Screening)

60 min4 focus topicstechnical

What to Expect

Conducted via video conference, this round combines behavioral questions with a technical case study. You'll start with behavioral questions about your background and past projects, then move into a product-focused SQL case study. The case study typically involves analyzing user engagement or product metrics using provided database schemas. You may be asked to write SQL queries, define KPIs, and explain your analytical approach. Finally, you'll discuss how you would present your findings to stakeholders. The interviewer is assessing your SQL proficiency, product thinking, communication clarity, and ability to structure an analysis around business questions.

Tips & Advice

Start by clarifying the business question before diving into SQL. Explain your approach aloud as you write queries—interviewers value seeing your thought process. Write clean, efficient SQL; avoid overly complex nested queries when simpler solutions exist. Define the KPIs you'd track and explain why those metrics matter for the product decision. Be ready to discuss tradeoffs in your analytical approach. Practice explaining technical SQL concepts in plain language. For senior level, discuss how you'd scale the analysis if data volume increased 100x, or how you'd ensure data quality and handle edge cases.

Focus Topics

Communication and Stakeholder Presentation

Practice explaining your findings clearly to non-technical stakeholders. Distill complex analyses into key insights and recommendations. Use data visualization concepts (visualizations you'd create to tell the story). Be concise and focus on business impact, not technical minutiae. For senior level, discuss how you'd handle scenarios where data contradicts stakeholder intuition, or how you'd influence decisions through data.

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Study Questions

Case Study Problem Solving and Structure

Approach case studies methodically: (1) clarify the business question, (2) break down the problem into sub-questions, (3) define relevant metrics/KPIs, (4) propose an analytical approach, (5) execute, (6) interpret results, (7) present recommendations. Practice explaining your logic aloud. Be comfortable with ambiguity and making reasonable assumptions when requirements are unclear. For senior level, discuss edge cases, data quality issues, and how to validate results.

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Metrics and KPI Definition

Learn to identify and define appropriate metrics for product decisions. Understand the difference between user-level metrics (e.g., DAU, retention) and event-level metrics. Know primary vs. guardrail metrics in A/B testing. Practice defining metrics for different product scenarios (e.g., measuring engagement with a new notification format, impact of a feature on user growth). For senior level, discuss metric hierarchy, leading vs. lagging indicators, and how to align metrics with business objectives.

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SQL Query Writing for Product Analytics

Master writing efficient SQL queries to extract meaningful insights from product data. Focus on JOINs (INNER, LEFT, RIGHT), aggregations, GROUP BY, HAVING clauses, window functions, and subqueries. Understand when to use each approach. Practice queries that calculate user engagement metrics, cohort analysis, retention rates, and funnel metrics. For senior level, optimize for readability and scalability. Be familiar with database design concepts and how to navigate complex schemas with multiple fact and dimension tables.

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Technical Skills Round (On-site)

90 min4 focus topicstechnical

What to Expect

This on-site round focuses on your technical depth in programming, data manipulation, and machine learning. You may be asked to write code in Python or R to solve data problems, implement machine learning algorithms, or optimize complex queries. The interviewer assesses your coding style, ability to handle edge cases, understanding of algorithmic complexity, and familiarity with common ML libraries like scikit-learn or TensorFlow. You may also be asked about feature engineering approaches, model selection criteria, and how to evaluate model performance. For senior level, expect discussions around scaling data processing, handling large datasets, and making design tradeoffs.

Tips & Advice

Write clean, well-commented code. Explain your approach before coding. Walk through examples and edge cases. For data problems, discuss time/space complexity and scalability considerations. Be comfortable with pandas, numpy, and scikit-learn for Python or tidyverse and caret for R. Know how to preprocess data (handling missing values, outliers, scaling). For ML questions, understand bias-variance tradeoff, overfitting prevention, and cross-validation. For senior level, discuss distributed data processing (Spark concepts), handling imbalanced datasets, and feature engineering at scale. Be ready to discuss when to use simple models vs. complex ones.

Focus Topics

Algorithm Complexity and Optimization

Understand big-O notation and how to analyze time/space complexity of algorithms. Know how to optimize code and data structures for performance. Discuss scalability considerations when dealing with large datasets. For senior level, discuss distributed computing concepts (MapReduce, Spark), when to use approximate algorithms for large-scale data, and how to design data pipelines that scale.

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Machine Learning Model Implementation

Know how to implement and tune common ML algorithms: regression, classification, clustering, and ensemble methods. Understand hyperparameter tuning (grid search, random search, Bayesian optimization). Be familiar with scikit-learn or equivalent libraries. Discuss model evaluation metrics appropriate for different problem types (accuracy, precision/recall, ROC-AUC, RMSE). For senior level, understand when to use complex models vs. simpler alternatives, regularization techniques (L1/L2), and how to prevent overfitting. Discuss the importance of validation strategy and data leakage.

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Advanced Python/R Programming

Demonstrate strong programming fundamentals: data structures, control flow, functions, and object-oriented principles. Be fluent in pandas (DataFrames, groupby, merge, apply) or R equivalents (dplyr, data.table). Write efficient, readable code with proper error handling. For senior level, discuss performance optimization, vectorization, and how to structure code for reusability and maintainability. Be familiar with common pitfalls (e.g., inplace operations, copy vs. reference) and how to profile code for bottlenecks.

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Data Manipulation and Feature Engineering

Master techniques for cleaning, transforming, and engineering features from raw data. Understand how to handle missing values, outliers, categorical encoding, scaling, and normalization. Practice creating domain-specific features that capture business logic. For senior level, discuss feature interaction effects, how to validate features, and how to prioritize features for interpretability vs. performance. Discuss dimensionality reduction techniques when dealing with high-dimensional data.

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Analytical Execution Round (On-site)

90 min4 focus topicstechnical

What to Expect

This round evaluates your practical ability to execute analyses using statistical and quantitative methods. You'll be asked product-focused questions that require you to define metrics, analyze data, and draw conclusions. The interviewer may provide a dataset or database schema and ask you to conduct an analysis to answer a business question. You'll be assessed on your ability to frame ambiguous problems, identify relevant metrics, apply appropriate statistical techniques, interpret results correctly, and communicate findings clearly. Expect questions about hypothesis testing, A/B test analysis, statistical significance, and how to handle common pitfalls like multiple comparisons or Simpson's paradox.

Tips & Advice

Start by clarifying the business question and defining success metrics before diving into analysis. Use statistical methods rigorously—understand p-values, confidence intervals, and effect sizes. Be comfortable explaining when results are practically significant vs. statistically significant. For A/B test questions, discuss sample size calculation, power analysis, and how long to run tests. Be aware of common statistical pitfalls (multiple comparison problem, survivorship bias, etc.). For senior level, discuss how to handle imbalanced metrics, segment analysis, and when simpler approaches outperform complex statistical methods. Practice explaining statistical concepts to a non-technical audience.

Focus Topics

Data Visualization and Insight Communication

Learn to communicate insights visually using charts, graphs, and dashboards. Understand which visualization type works best for different data distributions and messages. Practice translating raw analysis into executive-ready narratives. For senior level, discuss how to distill complex findings into key insights, anticipate stakeholder questions, and present recommendations confidently.

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A/B Test Analysis and Design

Master the end-to-end A/B testing process: hypothesis formation, sample size calculation, test duration, randomization validation, and result interpretation. Know how to calculate statistical power, MDE, and required sample size. Understand how to handle metrics with different sensitivities. For senior level, discuss advanced topics: sequential testing, novelty effects, long-term vs. short-term metrics, and how to design tests that account for network effects or interference between users.

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Hypothesis Testing and Statistical Rigor

Understand the fundamentals of hypothesis testing: null vs. alternative hypotheses, Type I and Type II errors, p-values, confidence intervals, and significance levels. Know when to use t-tests, chi-square tests, ANOVA, and non-parametric alternatives. Understand the assumptions underlying each test and what to do when assumptions are violated. For senior level, discuss power analysis, minimum detectable effect (MDE), and how to determine sample size requirements. Discuss the difference between statistical and practical significance.

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Metrics Definition and KPI Tracking

Learn to identify and define appropriate metrics for different product questions. Understand user-level metrics (DAU, MAU, retention, churn) vs. event-level metrics. Know the difference between primary metrics (what you're trying to improve) and guardrail metrics (what you want to protect). Practice writing clear metric definitions that can be implemented consistently. For senior level, discuss metric hierarchies, rollup strategies, and how to align metrics with business objectives.

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Analytical Reasoning Round (On-site)

90 min4 focus topicstechnical

What to Expect

This round assesses your ability to design rigorous research studies, reason through experimental design, identify potential biases, and solve complex analytical problems. You'll face ambiguous product questions and be expected to structure an experiment or analysis to test hypotheses. The interviewer evaluates your ability to frame research questions, design appropriate experiments, identify confounds and biases, apply causal inference thinking, and communicate findings through data storytelling. You may be asked to critique experimental designs, identify flaws in analyses, or propose solutions to tricky analytical challenges. For senior level, expect discussions around causality, research design tradeoffs, and how to influence strategic decisions with data.

Tips & Advice

When given a product question, take time to structure your thinking. Define the research question clearly, identify potential confounds, and propose an experimental design that isolates the effect of interest. Discuss tradeoffs in your approach (feasibility vs. rigor). Be comfortable with causal inference concepts: correlation vs. causation, confounding variables, and how randomization helps. Discuss when observational studies are appropriate vs. requiring randomized experiments. For senior level, discuss how to handle network effects in experiments, how to detect and measure spillover effects, and how to communicate statistical nuances to non-technical stakeholders. Practice identifying subtle biases in study designs presented by the interviewer.

Focus Topics

Data Storytelling and Executive Communication

Learn to weave analysis results into compelling narratives that drive decisions. Start with the business question and insight, then support with data. Use visualization and analogies to make complex findings accessible. Anticipate stakeholder questions and objections. For senior level, discuss how to communicate uncertainty, probabilistic thinking, and trade-offs in a way that executives can understand and act on.

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Study Questions

Research Design and Analytical Strategy

Learn to take ambiguous product questions and structure them into testable research hypotheses. Propose appropriate study designs (A/B tests, observational studies, quasi-experiments). Discuss tradeoffs: internal validity vs. external validity, statistical power vs. practical feasibility. For senior level, discuss how to design studies that scale, how to choose between multiple valid approaches, and how research constraints shape analytical strategy.

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Experiment Design and Causal Inference

Master the principles of experimental design: randomization, control groups, treatment groups, and how randomization enables causal inference. Understand when RCTs are feasible vs. when observational methods are necessary. Learn observational techniques like propensity score matching, instrumental variables, and regression discontinuity. For senior level, understand the causal inference framework, confounding variables, mediators vs. confounds, and how to identify when associations don't imply causation.

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Identifying Bias and Research Design Flaws

Learn to identify common biases: selection bias, survivorship bias, recall bias, social desirability bias. Understand how biases can invalidate findings. Practice critiquing study designs to spot flaws. For senior level, understand subtle biases like post-treatment bias, collider bias, and how to think systematically about bias sources in both experiments and observational studies.

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Behavioral Round (On-site)

60 min4 focus topicsbehavioral

What to Expect

This final on-site round assesses how well you fit Meta's culture and work style. The interviewer uses behavioral questions to understand how you've handled challenges in past roles, how you collaborate with teams, how you navigate ambiguity, and how you approach leadership. You'll be asked questions like 'Tell me about a time you influenced a stakeholder who disagreed with you' or 'Describe a project that didn't go as planned.' The interviewer is evaluating your judgment, communication skills, resilience, and alignment with Meta's values (like bias for action, move fast, and focus on impact). For senior level, expect questions about mentoring, driving initiatives across teams, and handling organizational challenges.

Tips & Advice

Use the STAR method: Situation, Task, Action, Result. Provide specific examples with measurable outcomes. Focus on your personal role and impact, not just team accomplishments. Be honest about failures and what you learned. Show self-awareness and growth mindset. For senior level, emphasize examples where you influenced others, mentored team members, drove cross-functional initiatives, or navigated ambiguous problems. Discuss how you balance moving fast with analytical rigor—a key tension at Meta. Research Meta's culture and leadership principles and reflect on how your experiences align.

Focus Topics

Handling Ambiguity and Complex Problems

Prepare examples of situations with unclear requirements or competing priorities. Discuss how you structured the problem, made reasonable assumptions, and moved forward despite ambiguity. For senior level, discuss how you've guided others through ambiguous situations and how you balance the desire for perfect information with the need to make decisions quickly.

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Meta-Specific Scenarios and Culture Alignment

Research Meta's culture, values, and current challenges. Prepare to discuss how your approach aligns with Meta's mission, its emphasis on speed and impact, and its approach to data-driven product development. Be ready for hypothetical scenarios about working at Meta (e.g., 'How would you handle a situation where leadership disagreed with your data analysis?'). Understand Meta's products (Facebook, Instagram, WhatsApp, Reality Labs) and think about data challenges they face.

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Past Project Experience and Impact

Prepare 3-5 concrete examples from your career where you had measurable impact: projects where you owned the analysis, influenced product decisions, or solved challenging problems. For each, prepare to discuss: (1) the business context, (2) the problem you solved, (3) your specific contributions, (4) the impact/results, and (5) what you learned. For senior level, emphasize examples where you took initiative, mentored others, or drove adoption of your findings across the organization.

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Cross-Functional Collaboration and Influence

Prepare examples of working effectively with engineers, product managers, and other teams. Discuss how you communicated findings to people outside your discipline, how you influenced decisions, and how you handled disagreements. For senior level, discuss examples where you drove alignment across multiple teams, navigated political dynamics, or championed unpopular but correct analyses.

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Frequently Asked Data Scientist Interview Questions

Experiment Design, Analysis, and Causal MethodsMediumTechnical

24 practiced

Design an experiment to evaluate a new search ranking algorithm where some users are logged in and others are anonymous. Decide on the randomization unit (user, session, request), discuss the pros/cons, propose primary and guardrail metrics, and outline how to compute sample size given baseline CTR and desired MDE.

Sample Answer

Clarify objective: measure causal effect of a new search ranking on user engagement (CTR) while avoiding bias from logged-in vs anonymous behavior.

Randomization unit — recommendation:- Primary: user-level randomization for logged-in users; session-level for anonymous users (hybrid).Pros/cons:- User-level: pros — avoids cross-contamination across sessions, captures persistent behavior; cons — requires identity, slower ramp if many anonymous users.- Session-level: pros — feasible for anonymous users, faster allocation; cons — risk of contamination if user sees both variants in different sessions.- Request-level: pros — maximal statistical power; cons — high risk of interference (same user sees inconsistent rankings), unrealistic UX inconsistency, violates SUTVA.Choose hybrid: assign stable treatment to logged-in user IDs; for anonymous, assign per-session with tracking cookie and expire after session. Ensure logging of assignment and stickiness windows.

Metrics:- Primary metric: per-search click-through rate (CTR) = clicks / searches. Use user-averaged CTR as main estimator to reduce heavy-tail influence.- Secondary metrics: mean clicks per search position, dwell time after click, query success rate (no reformulation), conversion rate (if applicable).- Guardrails: overall search latency, zero-results rate, query abandonment rate, user retention (7/28-day), and negative-tail CTR (for important segments).

Sample size:- For binary/ratio CTR approximate with proportions: required n per arm (searches or users) for detecting absolute MDE Δ with power 1-β and α:n = [ (Z_{1-α/2} sqrt(2 p (1-p)) + Z_{1-β} sqrt(p1(1-p1)+p2(1-p2)) )^2 ] / Δ^2Simpler approx when p1≈p2≈p: n ≈ 2 * (Z_{1-α/2}+Z_{1-β})^2 * p(1-p) / Δ^2Example: baseline CTR p=0.10, want 10% relative MDE → Δ=0.01 (absolute). For α=0.05 (Z=1.96), power=0.8 (Z=0.84):n ≈ 2*(1.96+0.84)^2 *0.1*0.9 / 0.01^2 ≈ 2*(7.84)*0.09 /1e-4 ≈ 1.411e5 searches per arm → ~141k searches/arm.Adjust for clustering (user-level): multiply by design effect = 1 + (m-1)ρ where m = avg searches per user, ρ = ICC. If interactions are correlated, increase n.

Analysis notes:- Pre-specify analysis (intention-to-treat), stratify by logged-in vs anonymous, and run heterogeneity checks.- Use sequential monitoring with alpha spending if checking early.- Monitor guardrails in near-real time and stop if severe regressions.

A and B Test DesignHardTechnical

45 practiced

You want to test three independent product changes A, B, and C simultaneously and detect pairwise interactions. Explain how to design a full factorial experiment (2^3), compute required sample size to detect main effects and interactions, describe analysis using regression/ANOVA, and explain how a significant interaction should influence rollout decisions.

Sample Answer

Situation & overview: Design a balanced 2^3 full factorial so each of A, B, C has two levels (e.g., control=−1 / treatment=+1 or 0/1). Randomize users into 8 cells (all combinations). Balanced design simplifies estimation: every main effect and interaction is orthogonal.

Design:- Cells: 8 combinations: (A±, B±, C±). Assign equal n users per cell.- Randomization: stratify or block (time, geo) if needed to reduce variance.- Metrics: pre-specify primary metric and its variance σ^2 (from historical data). Pre-specify detectable effect sizes for main effects and interactions.

Sample-size (closed form, balanced 2^3):- For a 2^k balanced design, the estimator variance of any main effect or any two-way/three-way interaction is Var(estimate) = σ^2 / (2^{k-1} n_cell). For k=3, Var = σ^2 / (4 n).- Standard error SE = sqrt(σ^2/(4 n_cell)).- To detect an effect Δ with two-sided significance α and power 1−β, require n_cell = (Z_{1−α/2} + Z_{1−β})^2 * σ^2 / (4 Δ^2).- Using standardized effect d = Δ/σ, equivalently: n_cell = (Z_{1−α/2} + Z_{1−β})^2 / (4 d^2).- Total N = 8 * n_cell.- Note: this same formula applies to main effects and interactions because contrasts have equal variance in balanced designs. If you expect interaction effect size Δ_int different from main effects, plug that Δ.

Example:- α=0.05 (Z=1.96), power=0.8 (Z=0.84), target d=0.1 ⇒ n_cell ≈ (1.96+0.84)^2 / (4 * 0.1^2) ≈ (2.8)^2 / 0.04 ≈ 7.84/0.04 = 196 ⇒ total N ≈ 1568.

Analysis (regression / ANOVA):- Fit linear model (coded −1/+1 or 0/1): Y = β0 + βA*A + βB*B + βC*C + βAB*A*B + βAC*A*C + βBC*B*C + βABC*A*B*C + ε- With −1/+1 coding, βA equals half the difference between A+ and A− averaged over other factors; orthogonality simplifies inference.- Use ANOVA or regression t/F tests to test: - Main effects: H0: βA = 0, etc. - Interactions: H0: βAB = 0, etc.- Check assumptions: residual homoscedasticity, normality (large-sample CLT often suffices), independence. Use robust SEs if heteroskedastic.- If binary/ratio metric, use appropriate GLM (logistic, Poisson), or analyze transformed metric; sample-size approximations then use delta method or simulation.

Interpreting a significant interaction and rollout decisions:- A significant interaction means the effect of one change depends on the level of the other. Do not average away interactions—main effects alone are misleading.- Follow-up: - Estimate and report simple effects: e.g., effect of A when B=0 vs B=1 (with C levels averaged or conditioned). - Visualize cell means (interaction plots) to see where benefits exist. - If interaction positive in some combinations and negative in others, only roll out the safe/improving combinations (conditional rollout). Example: A helps when B=0 but harms when B=1 → consider deploying A only for users not exposed to B, or avoid simultaneous rollout. - If interactions are small but significant due to large N, assess practical significance (business thresholds) before changing strategy. - Consider sequential experiments: if three-way interaction present, you may need targeted A/B tests on specific promising combinations to refine. - Use multiplicity control for multiple contrasts (e.g., Bonferroni, BH) or pre-specify primary contrasts.

Practical notes:- If variance σ^2 uncertain, run a pilot or use simulations to refine power.- If traffic limited, you can prioritize: detect larger effects or reduce α for exploratory interactions.- Always pre-register primary hypotheses (e.g., main effects) and treat interaction discovery as hypothesis-generating unless pre-specified.

This approach yields unbiased, orthogonal estimates of main and interaction effects; it gives a principled basis to decide conditional rollouts when interactions matter.

Data Storytelling and Insight CommunicationEasyTechnical

71 practiced

List and briefly explain five core principles of effective data visualization you would follow when preparing slides for an executive meeting. For each principle include a one-sentence example of its application and one recommended chart type or tool.

Hypothesis Testing and InferenceEasyTechnical

35 practiced

Order values are heavily right-skewed; you need to compare medians between two groups. Explain when the Mann-Whitney U test is appropriate versus a t-test, what each test actually compares, and limitations of interpreting Mann-Whitney as a test of medians.

Metric Definition and ImplementationMediumTechnical

58 practiced

Write a performant SQL query (BigQuery or Postgres – specify which) that computes daily DAU for the last 90 days using event_time (occured_at). Your solution should: 1) deduplicate users per day, 2) support partition pruning for performance, and 3) be robust to late-arriving events (explain approach). Include indexes/partitioning suggestions or table-clustering ideas.

Sample Answer

Approach: use BigQuery. Partition the events table by DATE(occured_at) (ingestion-time partitioning also possible) and cluster by user_id to enable partition pruning and fast per-user dedupe. Deduplicate per user/day with ROW_NUMBER() (avoids heavy COUNT(DISTINCT) on large windows). Handle late-arriving events by reprocessing/backfilling recent partitions (e.g., last 7 days) when computing DAU.

SQL (BigQuery):

sql

-- assumes events.table is partitioned by DATE(occured_at)
WITH dedup AS (
  SELECT
    DATE(occured_at) AS day,
    user_id
  FROM `project.dataset.events`
  WHERE DATE(occured_at) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE()
    -- partition pruning uses the date filter above
  QUALIFY ROW_NUMBER() OVER (PARTITION BY user_id, DATE(occured_at)
                            ORDER BY occured_at ASC) = 1
)
SELECT
  day,
  COUNT(1) AS dau
FROM dedup
GROUP BY day
ORDER BY day;

Why this is performant:- WHERE DATE(...) restricts scanned partitions (partition pruning).- QUALIFY + ROW_NUMBER() dedupes per user/day in one scan; avoids COUNT(DISTINCT) across many rows.- Clustering by user_id speeds the partition-local ROW_NUMBER().

Partitioning / clustering suggestions:- Partition on DATE(occured_at) (ingestion-time partitioning if event times can be massively late).- Cluster by user_id, maybe also by event_type.- Consider a daily aggregated materialized table (rolling 90-day) updated by a scheduled job that reprocesses last N days (e.g., 7 days) to capture late arrivals.

Handling late-arriving events:- Allow writes into past partitions and re-run the daily aggregation for a sliding window (e.g., re-compute last 7 days when job runs).- Optionally maintain an "events_staging" + CDC/backfill pipeline to reconcile late events and emit corrections to the aggregated table.

Feature Engineering and SelectionHardTechnical

26 practiced

As a staff-level data scientist you need to define team-wide feature engineering best practices and an approval process. Draft key policies covering feature creation standards, automated tests, documentation requirements, monitoring and alerting, versioning and rollbacks, and an approval workflow that ensures features are production-ready, auditable, and maintainable.

Sample Answer

Policy overview: establish a lightweight, auditable feature engineering (FE) governance that ensures reproducibility, quality, and safe deployment while minimizing friction.

1. Feature creation standards- Canonical definition: each feature is a single column produced by a named transform function with deterministic inputs.- Naming convention: team_prefix.entity.feature_name.version (snake_case).- Idempotency: transforms must be deterministic and re-runnable on historical data.- Data lineage: declare upstream sources, extraction SQL/ETL job, transformation code, and expected data types.

2. Automated tests- Unit tests: transform-level tests (synthetic and boundary cases).- Integration tests: end-to-end pipeline on a representative snapshot.- Data quality tests: null rate, unique cardinality, distribution drift checks.- Contract tests: schema/type checks and table naming/partitioning.

3. Documentation- Feature spec template: intent, math/formula, business use-cases, freshness SLA, expected cardinality, edge-case handling, privacy considerations.- README in feature repo + auto-generated doc from code comments.- Maintain an indexed feature registry searchable by name, owner, tags.

4. Monitoring & alerting- Production metrics: freshness latency, compute time, null/entropy monitors, downstream model impact (key metric deltas).- Alerting: severity levels (P1/P2) with on-call rotation and runbooks.

5. Versioning & rollbacks- Git for code; semantic versioning for feature artifacts.- Feature store holds immutable historical snapshots; new versions created as v2, with aliasing for downstream consumption.- Rollback process: if degradation detected, switch alias to previous stable version and trigger postmortem.

6. Approval workflow- CI gates: tests + DQ checks must pass.- Feature Proposal (PR) includes spec, tests, sample outputs, and cost estimate.- Reviewers: feature owner, data engineering, privacy, and at least one senior data scientist — 3-approver rule; automated checklist enforcement.- Staging deployment for 2-week shadow testing or A/B where applicable.- Once approved, feature promoted to production with metadata recorded in registry (owner, version, approval timestamp).

Governance enforcement: quarterly audits, metrics on review lead time, test coverage, and incidents. Provide templates, training, and a lightweight exceptions process for urgent needs.

Experiment Design, Analysis, and Causal MethodsMediumTechnical

34 practiced

You detect a covariate imbalance (e.g., prior spend) between treatment and control in an A/B test. Describe step-by-step analysis strategies to adjust for imbalance: covariate adjustment in regression, blocking/stratification, re-randomization, and post-stratification; explain pros/cons and when each is appropriate.

Sample Answer

Start by confirming the imbalance and its practical importance- Check summary stats, standardized mean difference (SMD), and visualize (histograms, boxplots). Test for chance imbalance (t-test, KS) but focus on effect size (SMD > 0.1 often meaningful). Check correlation of covariate with outcome—if weak, imbalance may not bias ATE.

Step-by-step strategies

1) Covariate adjustment via regression- What: Fit outcome ~ treatment + prior_spend + other covariates (linear, generalized linear, or ANCOVA). Report adjusted treatment coefficient and robust/clustered SEs.- Pros: Uses all data, increases precision, controls for multiple covariates, straightforward to implement.- Cons: Requires correct model specification (functional form, interactions), sensitive to outliers; may be biased if post-treatment covariates used.- When: Default when imbalance is modest and covariates are prognostic. Also good when you need a single summary estimator.

2) Blocking / stratified randomization (pre-experiment)- What: In future experiments, stratify randomization by prior_spend buckets so treatment assignment is balanced within strata.- Pros: Prevents imbalance, improves power, simple to explain.- Cons: Requires pre-specification, too many strata reduces randomization efficiency; operational complexity.- When: When covariate is strong predictor of outcome and you can assign users to strata before treatment.

3) Re-randomization (at design time)- What: Randomize, check balance; if imbalance exceeds threshold, discard and re-randomize until acceptable.- Pros: Guarantees balance by design.- Cons: Can complicate inference if not pre-specified; invalidates simple randomization assumptions unless accounted for; operational overhead.- When: Small experiments where pre-specification and documentation are feasible; used sparingly.

4) Post-stratification (analysis-time stratified estimator)- What: Partition data into strata by prior_spend, compute within-stratum treatment effects, and aggregate weighted by stratum sizes.- Pros: Nonparametric, robust to model misspecification; interpretable.- Cons: Requires sufficient sample per stratum; choice of strata affects variance; doesn't adjust for continuous covariates smoothly.- When: When covariate is strongly prognostic and you have large sample sizes per stratum.

Practical recommendations- Pre-specify adjustment plan in analysis plan. Prefer covariate adjustment (regression or ANCOVA) plus robust SEs as primary analytic approach; use post-stratification as sensitivity. For future tests, use blocking/stratification. Avoid ad-hoc re-randomization unless planned and documented. Report both unadjusted and adjusted ATEs and show sensitivity analyses.

A and B Test DesignMediumTechnical

91 practiced

Compare Bayesian A/B testing and frequentist hypothesis testing in the practical context of a growth team. Outline pros and cons for decision-making speed, interpretability, handling of interim monitoring, and prior information. Recommend when a Bayesian approach would be preferable for product experimentation.

Sample Answer

Situation: As a data scientist on a growth team, choosing the right testing framework affects how fast and confidently we roll out features. Below is a practical comparison and recommendation.

Decision-making speed:- Frequentist: Tests report p-values and require fixed sample sizes and pre-specified stopping rules to control Type I error. If you strictly follow the protocol, decisions are clear but can be slow (wait until N).- Bayesian: Produces posterior probabilities (e.g., P(treatment better than control) ) and expected loss; you can stop as soon as posterior crosses a decision threshold. This often enables faster, continuous decision-making.

Interpretability:- Frequentist: p-values and confidence intervals are commonly misunderstood (p-value ≠ probability hypothesis true). Interpretability for stakeholders can be poor without training.- Bayesian: Posterior probabilities and credible intervals map directly to intuitive statements (e.g., 95% chance lift > 0.5%). Easier to communicate business risk.

Interim monitoring:- Frequentist: Peeking inflates Type I error unless you apply corrections (alpha spending, Bonferroni, sequential tests), which complicates workflow.- Bayesian: Naturally accommodates optional stopping; posterior updates remain valid with continuous monitoring (assuming model and priors are correct), simplifying rolling analyses.

Prior information:- Frequentist: No formal mechanism to incorporate historical data; pooling requires hierarchical models or meta-analysis.- Bayesian: Priors let you encode historical experiments or expert beliefs, improving efficiency when priors are well-specified. Risk: misspecified or overly informative priors can bias results.

Pros & cons summary:- Frequentist pros: well-established, conservative error guarantees, simple for fixed-N designs. Cons: awkward with peeking, less intuitive, harder to incorporate past data.- Bayesian pros: intuitive outputs, flexible interim decisions, integrates priors and hierarchical modeling. Cons: requires model specification, sensitivity to priors, less familiar to stakeholders and some regulators.

Recommendation:Use Bayesian A/B testing for product experimentation when you need rapid decisions, frequent interim checks, or want to leverage historical data (e.g., many similar experiments, low-signal metrics). For high-stakes launches where regulatory-style Type I error guarantees matter or teams are not ready to validate priors/models, stick with frequentist approaches or use hybrid strategies (pre-registered frequentist core with Bayesian monitoring for early signals).

Data Storytelling and Insight CommunicationMediumTechnical

88 practiced

Describe three numerical techniques (for example, confidence intervals, bootstrapped estimates) and three visual techniques (for example, error bars, fan charts) you would use to communicate model uncertainty to product managers, and give a one-line example of how each technique aids decision-making.

Hypothesis Testing and InferenceHardTechnical

31 practiced

Design a simulation study to evaluate the Type I error and power of a new nonstandard test statistic under realistic data-generating processes that include heteroskedasticity, skewness, and missingness. Describe how you would select scenarios, generate data, run repeated simulations, compute evaluation metrics (Type I error, power, coverage), and present results to stakeholders.

Sample Answer

Goal: quantify empirical Type I error, power, and interval coverage of the new statistic under realistic departures from ideal assumptions (heteroskedasticity, skewness, and missingness), and communicate results with actionable recommendations.

1) Clarify requirements & constraints- Nominal alpha (e.g., 0.05), target power thresholds, realistic sample sizes, runtime limits.- Which estimand/test? One-sample mean, regression coefficient, etc.

2) Scenario selection (factorial design)- Sample sizes: small, medium, large (e.g., n = 50, 200, 1000).- Signal strengths: null (effect = 0) for Type I; several nonzero effects to map power (small/medium/large).- Heteroskedasticity patterns: homoskedastic; multiplicative var growing with predictor (var ∝ x^2); clustered/group heteroskedasticity.- Skewness: symmetric normal; moderate skew (e.g., log-normal); heavy tails (t with df=3).- Missingness mechanisms: MCAR (random drop p = 10%,30%), MAR (drop probability depends on covariate), MNAR (drop depends on outcome) — include imputation and complete-case approaches.- Covariate distributions and correlations if multivariate.

3) Data generation recipes (repeatable)- Set RNG seed; for each scenario: - Simulate covariates X (e.g., multivariate normal or categorical). - Generate errors ε from chosen distribution (normal/log-normal/t). - Impose heteroskedasticity: y = Xβ + σ(X) * ε with σ(X) specified. - Apply missingness: generate indicator R with logistic model for MAR/MNAR; set y missing where R=0.- Repeat M times (M=2000 recommended for ±1% Monte Carlo error; use fewer for prototyping).

4) Running the test- For each simulated dataset: - Apply preprocessing (impute if required). - Compute test statistic and p-value using the new method; also run benchmark methods (classic t-test, robust sandwich, bootstrap, permutation) for comparison. - If CI produced, record whether it covers true parameter.

5) Evaluation metrics- Type I error: proportion of simulations (under null) with p ≤ alpha.- Power: proportion of simulations (under alternative) with p ≤ alpha for each effect size.- Coverage: proportion of CIs covering true parameter.- Bias, empirical SE, RMSE, variance of estimator.- Convergence/failure rates and average runtime per sim.- Monte Carlo standard errors for each metric: sqrt(p(1-p)/M).

6) Implementation & computation- Parallelize simulations; save intermediate outputs; use fixed seeds per replicate for reproducibility.- Use nested loops over scenario factors and aggregate results into tidy table.

Example pseudocode (Python-like):

python

for scenario in scenarios:
    for rep in range(M):
        X = simulate_covariates(scenario)
        eps = simulate_errors(scenario)
        y = X.dot(beta) + sigma(X)*eps
        y = apply_missingness(y, X, scenario)
        y_imp = impute_if_needed(y, X, scenario)
        stat, pval, ci = run_new_test(y_imp, X)
        record_metrics(stat, pval, ci)
    summarize_metrics_for_scenario()

7) Diagnostics and sensitivity- Plot empirical Type I error vs nominal alpha; QQ plots of p-values under null.- Power curves vs effect size and sample size.- Coverage heatmaps across skew/heteroskedastic/missingness axes.- Display bias and RMSE tables.- Report Monte Carlo SE and mark scenarios where estimates are unstable.

8) Communicating to stakeholders- Executive summary: whether the test controls Type I error under realistic conditions; where it fails and how severe.- Visuals: concise set — Type I error barplots, power curves, coverage heatmaps, and a table of key scenarios with recommended practice (e.g., use bootstrap/robust SE or avoid when MNAR).- Recommendations: when to use the new statistic, suggested corrections (e.g., studentization, bootstrap), sample size guidance, and caveats.

9) Reproducibility & deliverables- Publish code, seeds, and scenario definitions; include notebook with selective simulation runs and final aggregated results.- Attach appendix with full scenario tables and Monte Carlo error estimates.

This design provides rigorous, interpretable evidence on Type I error, power, and coverage across realistic data-generating processes and supports data-driven guidance for adoption.

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